International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue I Jan 2023- Available at www.ijraset.com

V. RESULTS

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue I Jan 2023- Available at www.ijraset.com

The fruit was correctly identified by YOLO-V2 and the accuracy of our CNN classifier was 99.99% for the detection and classification of Soft rot and Phyllanthus emblica rust diseases of Phyllanthus emblica fruit. And the prompt eco-friendly treatment of disease was also printed. It was much faster and accurate method than the existing method, i.e SVM (Support Vector Machine), which is less computationally efficient and more time taking.

VI. CONCLUSIONS

In this paper, we have investigated a novel technique using CNN and YOLO V2 together with image processing techniques and deep learning algorithms for detection of Phyllanthus emblica fruit using images and classifying the Phyllanthus emblica fruit disease. It was the most effective strategy to identify the fruit and classify the fruit disease. Early fruit disease diagnosis is crucial for taking preventive measures to prevent it and restore the plant's health and reduce the risk of heavy crop destruction. Firstly, input image is taken from field using the ESP32 cam module and identifying fruit and then classifying different fruit diseases using images of fruit. YOLO V2 is used for identifying and detecting whether the input image is containing a Phyllanthus emblica fruit or not. If the image has fruit detected then classifying the Phyllanthus emblica fruit disease type from the input image and then we will specify the fungus, condition which the disease is favorable to grow and the eco-friendly treatment need to be implemented to restore the plant's health. Our proposed technique is better than the existing methods and effective method for detecting and classifying the fruit diseases.

Bibliography

Dr. Sachin Kumari H.O.D. Bio- Technology Department (GVM)

References

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